Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials
This work addresses the scarcity of high-quality training data for cathode materials in computational materials science, offering a more efficient approach for battery research.
The authors tackled the challenge of developing machine learning force fields for lithium-ion battery cathode materials by creating a multi-fidelity framework that uses both low- and high-fidelity datasets, achieving high-accuracy training at lower cost as demonstrated on the LMFP system.
Machine learning force fields (MLFFs), which employ neural networks to map atomic structures to system energies, effectively combine the high accuracy of first-principles calculation with the computational efficiency of empirical force fields. They are widely used in computational materials simulations. However, the development and application of MLFFs for lithium-ion battery cathode materials remain relatively limited. This is primarily due to the complex electronic structure characteristics of cathode materials and the resulting scarcity of high-quality computational datasets available for force field training. In this work, we develop a multi-fidelity machine learning force field framework to enhance the data efficiency of computational results, which can simultaneously utilize both low-fidelity non-magnetic and high-fidelity magnetic computational datasets of cathode materials for training. Tests conducted on the lithium manganese iron phosphate (LMFP) cathode material system demonstrate the effectiveness of this multi-fidelity approach. This work helps to achieve high-accuracy MLFF training for cathode materials at a lower training dataset cost, and offers new perspectives for applying MLFFs to computational simulations of cathode materials.